Data Stories: Dmitrii Vlasov on Kaggle Contests

At Gnip, we’re big fans of what the team at Kaggle is doing and have a fun time keeping tabs on their contests. One contest that I loved was held by WordPress and GigaOm to see what posts were most likely to generate likes, and we interviewed Dmitrii Vlasov who came in second in the Splunk Innovation Prospect and sixth overall. For me, it was interesting to speak to an up and coming data scientist who isn’t well known yet. Follow him at @yablokoff.

Dmitrii Vlasov of the GigaOm WordPress contest

1. You were recognized for your work in the first Kaggle contest you ever entered. What attracted you to Kaggle, and specifically the WordPress competition?

I came to Kaggle accidentally as it always happens. I read some blog post about the Million Song Dataset Challenge provided by Last.fm and bunch of other organizations. The task was to predict which songs will be liked by users based on their existing listening history. This immediately made me feel excited because I’m an active Last.fm user and was reflecting about what connections between people can be established based on their music preferences. But the contest was coming to end and so I switched to WordPress GigaOm contest and got 6th place there. Well, it is always interesting to predict something you already use.

2. What is your background in data science?

Now I’m a senior CS student in Togliatty, Russia. Can’t say that I have a special background in Data Science – I had more than a year-long course of probability theory and math statistics in university, some self-learned skills about semantic analysis and have big love to Python as a tool for implementing ideas. Also, I’ve entered the Machine Learning course on Coursera.

3. You found that blog posts with 30 to 50 pictures were more likely to be popular. You also found that longer blog posts also attract more likes (80,000-90,000 characters). This struck my marketing team as really high and was contrary to your hypothesis that longer content might be less viral. Why do you think this is?

Well, my numbers show relative correlation between amount of photos, characters and videos and the amount of likes received. Big relative “folks love” on several prominent amount of photos means that there were not so many posts with such amount of photos but most of them were qualitative. Quick empirical analysis shows that these are special type of posts – “big photo posts”. They usually are photo report, photo collection or scrapbook. For such types of posts 10-15 photos are not enough but at the same time 10-15 photos seem too overloaded for normal post. The same can be said about big amount of text in post. Of course, the most “likeable” posts contain 1,000-3,000 characters, but posts with 80-90 thousands are winners in “heavyweight category”. These are big researches, novels, political contemplation. Analyse is quite simple but it shows that if you want to create media-rich or text-rich content it should be really media-text-rich. Or you may fall in a hollow of not suitableness.

4. What else would like to predict with social data if you got the chance?

Now I work on romantic and friend relationships that could be established based on people’s music preferences (it’s a privately held startup in alpha). This is a really interesting and deep area! Also, I’d like to work with some political data e.g. to predict reaction on one or another politician’s statement based on a user’s Twitter feed. Or to extract all “real” thesis of politician based on all of his public speeches.